390 citations · 448 across the 4 of their papers we have counts for
6 papers
The added value for MRI radiomics and deep-learning for glioblastoma prognostication compared to clinical and molecular information
D. Abler, O. Pusterla, A. Joye-Kühnis +19
Background: Radiomics shows promise in characterizing glioblastoma, but its added value over clinical and molecular predictors has yet to be proven. This study assessed the added v…
Learning from spatially inhomogenous data: resolution-adaptive convolutions for multiple sclerosis lesion segmentation
Ivan Diaz, Florin Scherer, Yanik Berli +6
In the setting of clinical imaging, differences in between vendors, hospitals and sequences can yield highly inhomogeneous imaging data. In MRI in particular, voxel dimension, slic…
CHEmical-shift selective Adiabatic Pulse (CHEAP): Fast and High Resolution Downfield 3D 1H-MRSI at 7T
Guodong Weng, Piotr Radojewski, Sulaiman Sheriff +4
The key molecules such as triphosphate (ATP), glutathione (GSH), and homocarnosine (hCs) - central to metabolic processes in the human brain remain elusive or challenging to detect…
Federated Learning Enables Big Data for Rare Cancer Boundary Detection
Sarthak Pati, Ujjwal Baid, Brandon Edwards +276
Although machine learning (ML) has shown promise in numerous domains, there are concerns about generalizability to out-of-sample data. This is currently addressed by centrally shar…
QU-BraTS: MICCAI BraTS 2020 Challenge on Quantifying Uncertainty in Brain Tumor Segmentation - Analysis of Ranking Scores and Benchmarking Results
Raghav Mehta, Angelos Filos, Ujjwal Baid +89
Deep learning (DL) models have provided state-of-the-art performance in various medical imaging benchmarking challenges, including the Brain Tumor Segmentation (BraTS) challenges.…
Uncertainty-driven refinement of tumor-core segmentation using 3D-to-2D networks with label uncertainty
Richard McKinley, Micheal Rebsamen, Katrin Daetwyler +3
The BraTS dataset contains a mixture of high-grade and low-grade gliomas, which have a rather different appearance: previous studies have shown that performance can be improved by…